Is it Time Bayes went Fishing? Bayesian Probabilistic Reasoning in a Category Learning Task

Marcus Lindskog, Uppsala University, Uppsala, Uppsala, Sweden

Anders Winman, Uppsala University, Uppsala, Uppsala, Sweden

Peter Juslin, Uppsala University, Uppsala, Uppsala, Sweden

Abstract

People have generally been considered poor at probabilistic
reasoning, producing subjective probability estimates that far from accord to
normative rules. Features of the typical probabilistic reasoning task, however,
make strong conclusions difficult. The present study, therefore, combines
research on probabilistic reasoning with research on category learning where
participants learn base rates and likelihoods in a category-learning task. Later
they produce estimates of posterior probability based on the learnt
probabilities. The results show that our participants can produce subjective
probability estimates that are well calibrated against the normative Bayesian
probability and are sensitive to base rates. Further, they have accurate
knowledge of both base rate and means of the categories encountered during
learning. This indicates that under some conditions people might be better at
probabilistic reasoning than what could be expected from previous research.